Constructing multiple unique input/output sequences using metaheuristic optimisation techniques

Multiple unique input/output sequences (UIOs) are often used to generate robust and compact test sequences in finite state machine (FSM) based testing. However, computing UIOs is NP-hard. Metaheuristic optimisation techniques (MOTs) such as genetic algorithms (GAs) and simulated annealing (SA) are effective in providing good solutions for some NP-hard problems. In the paper, the authors investigate the construction of UIOs by using MOTs. They define a fitness function to guide the search for potential UIOs and use sharing techniques to encourage MOTs to locate UIOs that are calculated as local optima in a search domain. They also compare the performance of GA and SA for UIO construction. Experimental results suggest that, after using a sharing technique, both GA and SA can find a majority of UIOs from the models under test

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

open access articleThis article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

Mechanical characteristics, as well as physical-and-chemical properties of the slag-filled concretes, and investigation of the predictive power of the metaheuristic approach

Our article is devoted to development and verification of the metaheuristic optimisation algorithm in the course of selection of the compositional proportions of the slag-filled concretes. The experimental selection of various compositions and working modes, which ensure one and the same fixed level of a basic property, is the very labour-intensive and time-consuming process. This process cannot be feasible in practice in many situations, for example, in the cases, where it is necessary to investigate composite materials with equal indicators of resistance to aggressive environments or with equal share of voids in the certain range of dimensions. There are many similar articles in the scientific literature. Therefore, it is possible to make the conclusion on the topicality of the above-described problem. In our article, we will consider development of the methodology of the automated experimental-and-statistical determination of optimal compositions of the slag-filled concretes. In order to optimise search of local extremums of the complicated functions of the multi-factor analysis, we will utilise the metaheuristic optimisation algorithm, which is based on the concept of the swarm intelligence. Motivation in respect of utilisation of the swarm intelligence algorithm is conditioned by the absence of the educational pattern, within which it is not necessary to establish a certain pattern of learning as it is necessary to do in the neural-network algorithms. In the course of performance of this investigation, we propose this algorithm, as well as procedure of its verification on the basis of the immediate experimental verification. Open Access. © 2019 K. Borodin and N. Zhangabayuly Zhangabay, published by De Gruyter

Data Driven Approach to Enhancing Efficiency and Value in Healthcare

[eng] Healthcare is changing, and the era of data-driven healthcare organisations is increasingly popular. Data-Driven approaches (e.g., Machine Learning, Metaheuristics, Modelling and Simulation, Data Analytics, and Data Visualisation) can be used to increase efficiency and value in health services. Despite extensive research and technological development, the evidence impact of those methodologies in the healthcare sector is limited. In this Thesis we argue that an approach without borders in terms of academic societies and field of study could help to tackle this lack of impact to enhance efficiency and value in healthcare. This Thesis is based on solving practical problems in healthcare, with the research drawing upon both theoretical and empirical analysis. The research is organised in four stages. In the first, a variety of techniques from Modelling and Simulation were studied and used to analyse current performance and to model improved and more efficient future states of healthcare systems. The focus was the analysis of capacity, demand, activity, and queues both at hospital and population levels. In the second part, a Genetic Algorithm was used to solve a Routing Home Healthcare problem. In the third part, Social Network Analysis was used to visualise and analyse email networks. In the final part, a new healthcare system performance metric is proposed and implemented using a case study. New frameworks to implement these methodologies in the context of real-world problems are presented throughout the Thesis. In collaboration with University of Southampton, Wessex Academic Health Science Network (AHSN), and NHS England, several projects were developed and implemented for healthcare improvement in the UK. The work aims to increase early detection of cancer and thereby reduce premature mortality. The research was conducted working closely with NHS organisations across the Wessex region in England to produce bespoke endoscopy service modelling, as well as population level models. At a regional level, a Colorectal Cancer Screening Programme model was developed. An analysis of endoscopy activity, capacity and demand across the region was conducted. Future demand for endoscopy services in five years' time was estimated, and we found that the system has enough capacity to attend the expected future activity. A new healthcare system performance metric is presented as a tool to improve healthcare services. Genetic Algorithm metaheuristic was applied in a variant of the Home Health Care Problem (HHCP), focusing on the route planning of clinical homecare. Working with the Hospital del Mar Medical Research Institute of Barcelona and the Agency of Health Quality and Assessment of Catalonia a study was developed to estimate future utilisation scenarios of knee arthroplasty (KA) revision in the Spanish National Health System in the short-term (2015) and long-term (2030) and their impact on primary KA utilisation. One of the findings was that the variation in the number of revisions depended on both the primary utilisation rate and the survival function applied. Future activity and resources needed was estimated. A Social Network Analysis (SNA) project was developed in collaboration with the Wessex AHSN to analyse and extract insight from an organisational email, using SNA and Data Mining. A new healthcare system performance metric - based on the Overall Equipment Effectiveness (OEE) measure - is proposed and evaluated using real data from and Endoscopy Unit from a UK based hospital. To summarise, this work identifies four key techniques to use in the investigation of health data - Machine Learning Algorithms, Metaheuristic, Discrete Event Simulation and Data Analytics & Visualisations. Following a review of the different subjects and associated issues, those four techniques were evaluated and used with an applied-focus to solve healthcare problems. Key learning points from all different studies, as well as challenges and opportunities for the application of data-driven methodologies are discussed in the final chapter of the Thesis.[spa] La asistencia sanitaria está cambiando y la era de las organizaciones sanitarias basadas en datos es cada vez más popular. Los enfoques basados en datos (por ejemplo, Aprendizaje Automático; Meta-heurísticas; Modelamiento y Simulación; y Análisis y Visualización de datos) pueden utilizarse para aumentar la eficiencia y el valor en los servicios sanitarios. A pesar de la amplia investigación y el desarrollo tecnológico, la evidencia sobre el impacto de estas metodologías en el sector sanitario es limitada. En esta tesis argumentamos que un enfoque sin fronteras en términos de sociedades académicas y campo de estudio podría ayudar a abordar esta falta de impacto para aumentar la eficiencia y el valor en la asistencia sanitaria. Esta tesis se basa en la resolución de problemas prácticos en el sector sanitario, con un enfoque tanto teórico como práctico. La investigación se organizó en cuatro etapas. En la primera, una variedad de técnicas de modelamiento y simulación fueron estudiadas y aplicadas en el análisis y simulación de mejores y más eficientes configuraciones de sistemas sanitarios. El objetivo fue un análisis de capacidad, demanda, actividad y listas de esperas a nivel hospitalario y poblacional. En la segunda parte, un Algoritmo Genético fue implementado para resolver un problema de ruteo de personal sanitario encargado de atención de salud en el hogar. En la tercera parte, Análisis de Redes Sociales fue utilizado para visualizar y analizar una red de usuarios de correos electrónicos. En la etapa final, se propone una nueva métrica para evaluar el rendimiento de sistemas sanitarios, la cual fue implementada a través de un caso de estudio. Diferentes marcos de referencia para la implementación de estas metodologías en problemas reales se presentan a lo largo de la tesis

Selection and scaling validation of ground motions according to TBEC-2018 for the seismic assessment of masonry structures

This paper addresses the selection and scaling of earthquake time histories for analysing masonry structures' Out-Of-Plane (OOP) response according to the 2018 Turkish Building Earthquake Code (TBEC-2018) guidelines. Ground motion simulations are proposed for regions with limited seismic networks or lacking information regarding recorded accelerograms for large-magnitude events. Selection and scaling procedures are automatised according to the TBEC-2018 recommendations. The pre-selection is conducted according to specific seismological characteristics, and the optimal scaling factors of individual records are computed using a metaheuristic optimisation based on the Differential Evolution Method (DEM). Two sets of records (11 real and 11 simulated) are generated and used as input to conduct non-linear dynamic analyses. A U-shaped masonry prototype is adopted as a structural benchmark. The structural response is monitored with an emphasis on the OOP response.ERC -European Research Council(LA/P/0112/2020